Matryoshka Attribution: nested gradient-descent attribution tops interpretability benchmark by 2.9x
ChrisGPotts · x · 2026-09-24
A new paper introduces Matryoshka Attribution (MAttr), an attribution method that uses gradient descent to find which parts of a neural network drive a behavior, learning a nested ranking of model components across sparsities and directly optimizing intervention performance.
MAttr ranks #1 on the Mechanistic Interpretability Benchmark by a wide margin — 2.9× the runner-up — transfers across tasks, and extends to parameter attribution. Co-authors include Chris Potts, Dan Jurafsky, and Noah Goodman.
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